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dc.contributor.advisorRypdal, Martin
dc.contributor.advisorSirnes, Espen
dc.contributor.authorZankova, Ekaterina
dc.date.accessioned2016-05-30T08:39:18Z
dc.date.available2016-05-30T08:39:18Z
dc.date.issued2016-05-13
dc.description.abstractMachine learning is a rapidly evolving subfield of computer science. It has enormous amount of applications. One of the application domains is financial data analysis. Machine learning was usually applied for analysis and forecasting of daily financial time series. Availability of high frequency financial data became another challenge with its own specifics and difficulties. Regressors, being a significant part of machine learning field, have been selected as study subjects for this project. The purpose of this research is to apply machine learning techniques for predicting high frequency financial time series. Experiments are conducted using several regressors which are evaluated with respect to prediction quality and computation cost. The obtained results were analysed in order to reveal parameter combination for particular regressor that yields the best results according to chosen performance criteria.en_US
dc.identifier.urihttps://hdl.handle.net/10037/9255
dc.identifier.urnURN:NBN:no-uit_munin_8812
dc.language.isoengen_US
dc.publisherUiT Norges arktiske universiteten_US
dc.publisherUiT The Arctic University of Norwayen_US
dc.rights.accessRightsopenAccess
dc.rights.holderCopyright 2016 The Author(s)
dc.subject.courseIDMAT-3900
dc.subjectVDP::Mathematics and natural science: 400::Mathematics: 410::Applied mathematics: 413en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Matematikk: 410::Anvendt matematikk: 413en_US
dc.subjectmachine learningen_US
dc.subjectpredictionen_US
dc.subjecthigh frequencyen_US
dc.subjectfinancial dataen_US
dc.subjectregressionen_US
dc.titleHigh frequency financial time series prediction: machine learning approachen_US
dc.typeMaster thesisen_US
dc.typeMastergradsoppgaveen_US


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